PostgreSQL Setup and Configuration#

Every PostgreSQL deployment boils down to three things: get the binary running, configure who can connect, and tune the memory settings.

Installation Methods#

Package Managers#

On Debian/Ubuntu, use the official PostgreSQL APT repository:

sudo apt install -y postgresql-common
sudo /usr/share/postgresql-common/pgdg/apt.postgresql.org.sh
sudo apt install -y postgresql-16

On macOS: brew install postgresql@16 && brew services start postgresql@16

On RHEL/Fedora:

sudo dnf install -y https://download.postgresql.org/pub/repos/yum/reporpms/EL-9-x86_64/pgdg-redhat-repo-latest.noarch.rpm
sudo dnf install -y postgresql16-server
sudo /usr/pgsql-16/bin/postgresql-16-setup initdb
sudo systemctl enable --now postgresql-16

Config files live at /etc/postgresql/16/main/ (Debian) or /var/lib/pgsql/16/data/ (RHEL).

SQLite for Production Use#

SQLite is not a toy database. It handles more read traffic than any other database engine in the world – every Android phone, iOS device, and major web browser runs SQLite. The question is whether your workload fits its concurrency model: single-writer, multiple-reader. If it does, SQLite eliminates an entire class of operational overhead with no server process, no network protocol, and no connection authentication.

WAL Mode#

Write-Ahead Logging (WAL) mode is the single most important configuration for production SQLite. In the default rollback journal mode, writers block readers and readers block writers. WAL removes this limitation.

Time-Series Database Selection and Operations#

Time-series databases optimize for a specific access pattern: high-volume writes of timestamped data points, queries that aggregate over time ranges, and automatic expiration of old data. Choosing the right one depends on your data model, query patterns, retention requirements, and operational constraints.

When You Need a Time-Series Database#

A dedicated time-series database is justified when you have high write throughput (thousands to millions of data points per second), queries that are predominantly time-range aggregations, and data that has a defined retention period. Common use cases: infrastructure metrics, application performance monitoring, IoT sensor data, financial tick data, and log analytics.

Choosing a Database Strategy#

Every Kubernetes-based platform eventually faces two questions: should the database run inside the cluster or as a managed service, and which database engine fits the workload? These decisions are difficult to reverse. A database migration is one of the highest-risk operations in production. Getting the initial decision roughly right saves months of future pain.

Where to Run: Kubernetes vs Managed Service#

This is not a technology question. It is an organizational question about who owns database operations and what tradeoffs the team will accept.

Database Connection Pooling: PgBouncer, ProxySQL, and Application-Level Patterns#

Database connections are expensive resources. PostgreSQL forks a new OS process for every connection. MySQL creates a thread. Both allocate memory for session state, query buffers, and sort areas. When your application scales horizontally in Kubernetes – 10 pods, then 20, then 50 – the connection count multiplies, and most databases buckle long before your application pods do.

Connection pooling solves this by maintaining a smaller set of persistent connections to the database and sharing them across many application clients. Understanding pooling options, deployment patterns, and sizing is essential for any production database workload on Kubernetes.

Elasticsearch and OpenSearch: Indexing, Queries, Cluster Management, and Performance#

Elasticsearch and OpenSearch are distributed search and analytics engines built on Apache Lucene. They excel at full-text search, log aggregation, metrics storage, and any workload that benefits from inverted indices. Understanding index design, mappings, query mechanics, and cluster management separates a working setup from one that collapses under production load.

Elasticsearch vs OpenSearch#

OpenSearch is the AWS-maintained fork of Elasticsearch, created after Elastic changed its license from Apache 2.0 to the Server Side Public License (SSPL) in early 2021. For the vast majority of use cases, the two are interchangeable. APIs are compatible, concepts are identical, and most tooling works with both. OpenSearch Dashboards replaces Kibana. This guide applies to both unless explicitly noted.

Redis Deep Dive: Data Structures, Persistence, Performance, and Operational Patterns#

Redis is an in-memory data store, but calling it a “cache” undersells what it can do. It is a data structure server that happens to be extraordinarily fast. Understanding its data structures, persistence model, and operational characteristics determines whether Redis becomes a reliable part of your architecture or a source of mysterious production incidents.

Data Structures Beyond Key-Value#

Redis supports far more than simple string key-value pairs. Each data structure has specific use cases where it outperforms alternatives.

Redis on Kubernetes: Deployment Patterns, Operators, and Production Configuration#

Running Redis on Kubernetes requires more thought than deploying a stateless application. Redis is stateful, memory-sensitive, and its clustering model makes assumptions about network identity that conflict with Kubernetes defaults. This guide covers the deployment options from simplest to most complex, the configuration details that matter in production, and the mistakes that cause outages.

Deployment Options#

There are three main approaches to deploying Redis on Kubernetes, each with different tradeoffs.